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Hybridization of Taguchi method and genetic algorithm to optimize a PVT in different Moroccan climatic zones

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  • Ben Seddik, Z.
  • Ben Taher, M.A.
  • Laknizi, A.
  • Ahachad, M.
  • Bahraoui, F.
  • Mahdaoui, M.

Abstract

Using a hybrid approach of Taguchi's method and genetic algorithm, an optimization study was carried out to improve the electrical and thermal efficiencies of a photovoltaic thermal water-based collector (PVT) under the weather conditions of the climate zones of Morocco. First, a mathematical model was developed and validated to predict both of the thermal and the electrical performance of the panel under different weather conditions. Then, The Taguchi method was used to reduce the optimization size where the parameters with the highest influence in the panel performance were identified. Bi-objective optimizations were carried out for the six climate zones using the genetic algorithm, where the electrical and the thermal efficiencies were the fitness functions. In order to assess the improvement in the PVT panel with optimized parameters performance, a PVT baseline design with un-optimized parameters was chosen, and a comparison study was conducted based on monthly, annually energy and environmental performance. The results showed that the PVT panel with the optimized parameters is capable of producing higher average monthly energy almost in the six climate zones, and the annual gain achieved varies from 15.5% to 19% depending on the climate zone. This allows higher CO2 emissions mitigations compared to un-optimized PVT.

Suggested Citation

  • Ben Seddik, Z. & Ben Taher, M.A. & Laknizi, A. & Ahachad, M. & Bahraoui, F. & Mahdaoui, M., 2022. "Hybridization of Taguchi method and genetic algorithm to optimize a PVT in different Moroccan climatic zones," Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222007058
    DOI: 10.1016/j.energy.2022.123802
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    References listed on IDEAS

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    Cited by:

    1. Dong, Shiqian & Long, He & Guan, Jingxuan & Jiang, Lina & Zhuang, Chaoqun & Gao, Yafeng & Di, Yanqiang, 2024. "Performance investigation of a hybrid PV/T collector with a novel trapezoidal fluid channel," Energy, Elsevier, vol. 288(C).
    2. Afzal, Asif & Buradi, Abdulrajak & Jilte, Ravindra & Shaik, Saboor & Kaladgi, Abdul Razak & Arıcı, Muslum & Lee, Chew Tin & Nižetić, Sandro, 2023. "Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    3. Mortadi, M. & El Fadar, A. & Achkari Begdouri, O., 2024. "4E analysis of photovoltaic thermal collector-based tri-generation system with adsorption cooling: Annual simulation under Moroccan climate conditions," Renewable Energy, Elsevier, vol. 221(C).
    4. Ben Taher, M.A. & Pelay, U. & Russeil, S. & Bougeard, D., 2023. "A novel design to optimize the optical performances of parabolic trough collector using Taguchi, ANOVA and grey relational analysis methods," Renewable Energy, Elsevier, vol. 216(C).
    5. Chdil, O. & Bikerouin, M. & Balli, M. & Mounkachi, O., 2023. "New horizons in magnetic refrigeration using artificial intelligence," Applied Energy, Elsevier, vol. 335(C).

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